People generally have a need for organizational tools to manage their busy lives. Such tools have, in the past, consisted of clumsy paper-based systems, and more recently have begun to make use of computer technology. Today, portable personal assistant devices, such as smartphones and personal digital assistants (PDAs), exist to help people keep track of information, scheduling, appointments, reminders, contact information and myriad other bits of data. Such devices can present prescheduled alerts and reminders to the user. Many of the latest devices can synchronize their data with remote servers, and receive messages and instructions from remote sources. However, these devices generally do not have any sort of built-in intelligence. They are unable to adapt to the behavior, environment or needs of the user. The user has to explicitly instruct the device to perform any specific action. This limits the usefulness of such devices as personal assistants, as the user must constantly monitor the function of the device to ensure it has all the correct data and has been programmed to perform the correct actions at the correct times.
It should be noted that Patent 61/445,433 includes concepts related to the invention described herein.
Hands-free computing devices, such as Bluetooth headsets, exist that allow the user to interact with a computer or mobile device, giving commands and receiving information. Rudimentary neural interfaces even exist that can detect commands and responses indicated by the user's thoughts and emotions. However, none of these hands-free devices is capable of connecting directly and immediately to a more distributed source of processing power, such as a “cloud” of networked devices available for computing, and none is capable of adapting its behavior to the user's current circumstances—environment, status, activity, etc.—or predicting the user's desires and proactively consulting the user regarding potentially desirable actions and information.
In using a neural interface, the user thinks a particular thought or expresses a particular emotion to register a particular input, such as “yes” or “no.” An example of such a device is the NeuroSky MindSet. These neural devices, however, can be difficult to learn to use, they require a great deal of concentration to ensure the correct thought is brought to mind or the correct emotion expressed, and they are quite slow in terms of how quickly the user can register an input. The level of concentration necessary precludes use of a neural interface while performing other tasks requiring concentration, such as driving a car, holding a conversation or operating machinery. Further, the difficulty of registering an unambiguous stimulus to the interface makes these devices useful only for the simplest and least critical applications. These are severe limitations that prevent many potential uses for neural control devices.
Transcription services exist whereby a user can call a specific telephone number, speak a message and have the message sent to any of a number of destinations, including an email address, a blog and the microblogging service Twitter. The limitation of these services, however, is that the user must call a number and wait for a connection before speaking his or her message, which consumes a great deal of extra time and requires extra focused attention.
Predictive dialers exist that automatically call telephone numbers, play a recorded message, wait for a response from the recipient of the phone call, whether via keypad entry on the telephone or via interactive voice response (IVR), and then play another recorded message or transfer the recipient to a particular telephone number based on the response. But these dialers are fixed in their functionality, cannot ask questions autonomously and cannot automatically adapt to the user's preferences and behavior.
Limited data prioritization systems exist, such as Gmail's Priority Inbox, that adaptively sort information based on relevance or importance. However, these systems are generally limited to specific domains and do not operate broadly enough to be useful in a true personal assistant system.
Numerous services exist that present recommendations (such as product recommendations) based on historical and “crowd-sourced” data—data gathered from the behavior of large groups of users—such as Hunch.com, Mint.com and Amazon.com. However, none of these services is sophisticated enough to support a true personal assistant system.
Systems have been previously described that estimate a user's current “state” in terms of activity and availability, but none of these systems use the resulting conclusions to provide the adaptive interaction and decision-making capabilities of a true personal assistant. Further, some systems make possible semi-automated scheduling using some level of automatic decision-making capability, but these systems are limited in scope and fall short of the qualities desirable in a true electronic personal assistant. Other systems exist that make use of distributed data sources and the user's current context to provide relevant information, but these too fall short of a true personal assistant in that they simply provide information and do not provide the decision-making capabilities desired of a personal assistant.
Live, remote personal assistant services exist that provide the functions of a personal assistant to many customers for a fee, and operate via phone, email or other communication channels. However, these services are just a different way of providing traditional personal assistant services, and still require that a live individual perform the services. The automation of services is not present, as is desirable in a true electronic personal assistant system. Further, as the customer usually does not know the hired assistant personally, lack of trust and close communication can present obstacles to effective service.
When being called on to make decisions, executives usually prefer simple, clear proposals, providing all relevant data and requiring approval or disapproval (or possibly deferral). This reduces the burden on the executive and increases efficiency. However, existing digital assistant devices fail to meet this need, requiring a human operator to find, evaluate and analyze information. Thus, the executive either has to do the work himself or hire a live assistant to manage his digital assistant, effectively defeating the purpose.
The present invention has the object of solving these problems.